• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.121-131
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• 2007

In this paper, we examine the problem of estimating the sensitive characteristics and behaviors in a multinomial randomized response model using Bayesian approach. We derived a posterior distribution for parameter of interest for multinomial randomized response model. Based on the posterior distribution, we also calculated a credible intervals and mean squared error (MSE). We finally compare the maximum likelihood estimator and the Bayes estimator in terms of MSE.

• Communications for Statistical Applications and Methods
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• 제24권3호
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• pp.193-209
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• 2017

The power Lindley distribution with some of its properties is considered in this article. Maximum likelihood, least squares, maximum product spacings, and Bayes estimators are proposed to estimate all the unknown parameters of the power Lindley distribution. Lindley's approximation and Markov chain Monte Carlo techniques are utilized for Bayesian calculations since posterior distribution cannot be reduced to standard distribution. The performances of the proposed estimators are compared based on simulated samples. The waiting times of research articles to be accepted in statistical journals are fitted to the power Lindley distribution with other competing distributions. Chi-square statistic, Kolmogorov-Smirnov statistic, Akaike information criterion and Bayesian information criterion are used to access goodness-of-fit. It was found that the power Lindley distribution gives a better fit for the data than other distributions.

• 품질경영학회지
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• 제27권1호
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• pp.80-90
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• 1999

In this paper, reliability estimation using Gibbs sampler is considered for the mixture model with Gamma family, Gibbs sampler is derived to compute the features for the posterior distribution. By simulation study, the maximum likelihood estimator and the Gibbs estimator are obtained. A numerical study with a simulated data is provided.

• Communications for Statistical Applications and Methods
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• 제27권1호
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• pp.47-64
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• 2020

In this paper, we derive some estimators of the scale parameter of the exponentiated half-logistic distribution based on the multiply Type-I hybrid censoring scheme. We assume that the shape parameter λ is known. We obtain the maximum likelihood estimator of the scale parameter σ. The scale parameter is estimated by approximating the given likelihood function using two different Taylor series expansions since the likelihood equation is not explicitly solved. We also obtain Bayes estimators using prior distribution. To obtain the Bayes estimators, we use the squared error loss function and general entropy loss function (shape parameter q = -0.5, 1.0). We also derive interval estimation such as the asymptotic confidence interval, the credible interval, and the highest posterior density interval. Finally, we compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation. The average length of 95% intervals and the corresponding coverage probability are also obtained.

• Communications for Statistical Applications and Methods
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• 제27권4호
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• pp.413-430
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• 2020

The multiply Type-II hybrid censoring scheme is disadvantaged by an experiment time that is too long. To overcome this limitation, we propose a generalized multiply Type-II hybrid censoring scheme. Some estimators of the scale parameter of the exponential distribution are derived under a generalized multiply Type-II hybrid censoring scheme. First, the maximum likelihood estimator of the scale parameter of the exponential distribution is obtained under the proposed censoring scheme. Second, we obtain the Bayes estimators under different loss functions with a noninformative prior and an informative prior. We approximate the Bayes estimators by Lindleys approximation and the Tierney-Kadane method since the posterior distributions obtained by the two priors are complicated. In addition, the Bayes estimators are obtained by using the Markov Chain Monte Carlo samples. Finally, all proposed estimators are compared in the sense of the mean squared error through the Monte Carlo simulation and applied to real data.

• Journal of applied mathematics & informatics
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• 제6권2호
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• pp.661-668
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• 1999

Reliability estimation using Gibbs sampler considered for modeling mixture exponential reliability problems. Gibbs sampler is developed to compute the features of the posterior distribution. Bayesian estimation of complicated functions requires simpler esti-mation techniques due to the mathematical difficulties involved in the Bayes approach. The Maximum likelihood estimator and the Gibbs estimator of reliability of the system are derived. By simula-tion risk behaviors of derived estimators are compared. model de-termination based on relative error is considered. A numerical study with a simulated data set is provided.

• 한국정보전자통신기술학회논문지
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• 제9권2호
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• pp.190-195
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• 2016

본 연구에서는 균일 선형 배열 안테나 시스템에서 입사신호의 방향을 추정하기 위한 기존의 방법과 제안방법의 성능에 대해서 비교한다. 본 논문에서 제안한 방법은 최대 사후 확률 추정기를 적용하여 신호의 도래방향 추정 오차확률을 감소하고자 한다. 신호 추정 방향 확률 오차를 감소시키면 안테나에 입사하는 신호의 방향을 정확히 추정할 수 있다. 모의실험을 이용하여 본 연구에서 제안한 방법과 기존의 방법을 비교 분석하였으며 또한 배열 안테나 개수를 증가시키면서 신호 추정 오차 확률을 비교 분석하였다. 본 연구에서 제안한 방법이 기존의 방법보다 약 8%의 신호 추정 오차 확률을 감소시켜 도래방향 신호 추정 능력이 우수함을 입증하였다.

• Journal of the Korean Data and Information Science Society
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• 제7권2호
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• pp.247-256
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• 1996

본 논문은 깁스 표본 기법을 이용하여 Demster et al.(1981)에 의해 소개된 Field Mice자료를 분석하기 위하여 베이지안 계층적 모형을 적용시켜 보았다. Jeffrey의 사전확률을 이용한 사후 평균을 깁스 표본 기법을 이용하여 구하였고, 이로 부터 얻은 베이지안 추정량을 최소 자승 추정량, EM알고리즘을 이용한 랜덤 효과를 포함한 가능도함수에 대한 최대 가능도 추정량(MLR)과 비교하였다.

• 대한전자공학회논문지SP
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• 제49권2호
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• pp.69-75
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• 2012

본 논문은 MAP (maximum a posterior) 예측기에 기반하여 움직임 보상 예측 오차를 보정해주는 방식의 순차주사화 (de-interlacing) 알고리즘을 제안한다. 먼저, 현재 필드와 인접한 필드 간의 적절한 정합 (registration)을 수행 한 후, 계산된 정합 정보에 기반한 MAP 예측기를 통해 현재 필드에 대응하는 순차 주사 (progressive) 프레임을 찾아낸다. 안정적인 결과를 얻기 위하여 잘 알려진 BTV (bilateral total variation) 기반의 평활화 (regularization) 과정이 추가된다. 한편, 잘못된 정합 정보로 인한 소위 깃털 현상 (feathering artifact)을 억제하기 위하여 블록 단위로 깃털 현상 발생 여부를 판단하여 발생되었다고 판단된 블록 영역에 대해서는 앞서 설명한 MAP기반 순차주사화 대신 에지 방향성에 기반한 공간적 순차주사화를 적용한다. 실험 결과에 따르면, 제안된 기법은 종래 기법들에 비하여 평균 약 4dB의 PSNR 성능 개선을 보이고 있으며, 우수한 주관적 화질을 보여주고 있다.

• Journal of the Korean Statistical Society
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• 제28권1호
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• pp.73-92
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• 1999

In Bayesian model selection or testing problems, one cannot utilize standard or default noninformative priors, since these priors are typically improper and are defined only up to arbitrary constants. The resulting Bayes factors are not well defined. A recently proposed model selection criterion, the intrinsic Bayes factor overcomes such problems by using a part of the sample as a training sample to get a proper posterior and then use the posterior as the prior for the remaining observations to compute the Bayes factor. Surprisingly, such Bayes factor can also be computed directly from the full sample by some proper priors, namely intrinsic priors. The present paper explains how to derive intrinsic priors for simple tree ordered exponential means. Some numerical results are also provided to support theoretical results and compare with classical methods.